{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:GVOU4JMNEO2RDDVZEXM2ILJTMB","short_pith_number":"pith:GVOU4JMN","canonical_record":{"source":{"id":"1908.11820","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-30T16:18:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5745eed95e9ad65b8092b8767522b4f628f5dad55fd92df35f7ccf2a453b0fea","abstract_canon_sha256":"ef171bf9ef4358707c53b75518d44af20f16755a384bc025069eaa3bcf9a6819"},"schema_version":"1.0"},"canonical_sha256":"355d4e258d23b5118eb925d9a42d33606185af5dd12841e5ad3591e6cae7839d","source":{"kind":"arxiv","id":"1908.11820","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.11820","created_at":"2026-07-05T00:00:46Z"},{"alias_kind":"arxiv_version","alias_value":"1908.11820v1","created_at":"2026-07-05T00:00:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.11820","created_at":"2026-07-05T00:00:46Z"},{"alias_kind":"pith_short_12","alias_value":"GVOU4JMNEO2R","created_at":"2026-07-05T00:00:46Z"},{"alias_kind":"pith_short_16","alias_value":"GVOU4JMNEO2RDDVZ","created_at":"2026-07-05T00:00:46Z"},{"alias_kind":"pith_short_8","alias_value":"GVOU4JMN","created_at":"2026-07-05T00:00:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:GVOU4JMNEO2RDDVZEXM2ILJTMB","target":"record","payload":{"canonical_record":{"source":{"id":"1908.11820","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-30T16:18:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5745eed95e9ad65b8092b8767522b4f628f5dad55fd92df35f7ccf2a453b0fea","abstract_canon_sha256":"ef171bf9ef4358707c53b75518d44af20f16755a384bc025069eaa3bcf9a6819"},"schema_version":"1.0"},"canonical_sha256":"355d4e258d23b5118eb925d9a42d33606185af5dd12841e5ad3591e6cae7839d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:00:46.561578Z","signature_b64":"1yrYRe4XGKMGCj2dbOa8IK6kaOz/us3ld/ccrFkL3kbZYnEp9VlVvcmVWp2snDwX2sfFOf9dQf5QLmQ5J4SZAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"355d4e258d23b5118eb925d9a42d33606185af5dd12841e5ad3591e6cae7839d","last_reissued_at":"2026-07-05T00:00:46.561088Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:00:46.561088Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.11820","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:00:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kTVcfUnGVLyyRpRF09fDMYwvIWL/YMO0RqkewT9iIcoU8S4t50Uu9C3wfbKWwBX/9dZWZCloGtLr6uOTnnKgDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T02:13:17.170721Z"},"content_sha256":"e9d6475f93cd77d3259589c31f1ed0540c7cea9d810f19cd522e94341cc6ec17","schema_version":"1.0","event_id":"sha256:e9d6475f93cd77d3259589c31f1ed0540c7cea9d810f19cd522e94341cc6ec17"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:GVOU4JMNEO2RDDVZEXM2ILJTMB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Rich Representations For Structured Visual Prediction Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Mohammadreza Mostajabi","submitted_at":"2019-08-30T16:18:26Z","abstract_excerpt":"We describe an approach to learning rich representations for images, that enables simple and effective predictors in a range of vision tasks involving spatially structured maps. Our key idea is to map small image elements to feature representations extracted from a sequence of nested regions of increasing spatial extent. These regions are obtained by \"zooming out\" from the pixel/superpixel all the way to scene-level resolution, and hence we call these zoom-out features. Applied to semantic segmentation and other structured prediction tasks, our approach exploits statistical structure in the im"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.11820","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.11820/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:00:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9tl7aS5Ypz0NmEPfE9qSS+JZfXQkiLlaTGtB8XvpWX2CEDAzcyVBRbbaaK0y0/bp23AczPe3Q/+nYjYtzXGRCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T02:13:17.171219Z"},"content_sha256":"2c4857e992c5c76122cd0d7aa824f1fc0095526be7ea447a9b473a222ffb2579","schema_version":"1.0","event_id":"sha256:2c4857e992c5c76122cd0d7aa824f1fc0095526be7ea447a9b473a222ffb2579"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GVOU4JMNEO2RDDVZEXM2ILJTMB/bundle.json","state_url":"https://pith.science/pith/GVOU4JMNEO2RDDVZEXM2ILJTMB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GVOU4JMNEO2RDDVZEXM2ILJTMB/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-21T02:13:17Z","links":{"resolver":"https://pith.science/pith/GVOU4JMNEO2RDDVZEXM2ILJTMB","bundle":"https://pith.science/pith/GVOU4JMNEO2RDDVZEXM2ILJTMB/bundle.json","state":"https://pith.science/pith/GVOU4JMNEO2RDDVZEXM2ILJTMB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GVOU4JMNEO2RDDVZEXM2ILJTMB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:GVOU4JMNEO2RDDVZEXM2ILJTMB","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ef171bf9ef4358707c53b75518d44af20f16755a384bc025069eaa3bcf9a6819","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-30T16:18:26Z","title_canon_sha256":"5745eed95e9ad65b8092b8767522b4f628f5dad55fd92df35f7ccf2a453b0fea"},"schema_version":"1.0","source":{"id":"1908.11820","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.11820","created_at":"2026-07-05T00:00:46Z"},{"alias_kind":"arxiv_version","alias_value":"1908.11820v1","created_at":"2026-07-05T00:00:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.11820","created_at":"2026-07-05T00:00:46Z"},{"alias_kind":"pith_short_12","alias_value":"GVOU4JMNEO2R","created_at":"2026-07-05T00:00:46Z"},{"alias_kind":"pith_short_16","alias_value":"GVOU4JMNEO2RDDVZ","created_at":"2026-07-05T00:00:46Z"},{"alias_kind":"pith_short_8","alias_value":"GVOU4JMN","created_at":"2026-07-05T00:00:46Z"}],"graph_snapshots":[{"event_id":"sha256:2c4857e992c5c76122cd0d7aa824f1fc0095526be7ea447a9b473a222ffb2579","target":"graph","created_at":"2026-07-05T00:00:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1908.11820/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We describe an approach to learning rich representations for images, that enables simple and effective predictors in a range of vision tasks involving spatially structured maps. Our key idea is to map small image elements to feature representations extracted from a sequence of nested regions of increasing spatial extent. These regions are obtained by \"zooming out\" from the pixel/superpixel all the way to scene-level resolution, and hence we call these zoom-out features. Applied to semantic segmentation and other structured prediction tasks, our approach exploits statistical structure in the im","authors_text":"Mohammadreza Mostajabi","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.11820","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e9d6475f93cd77d3259589c31f1ed0540c7cea9d810f19cd522e94341cc6ec17","target":"record","created_at":"2026-07-05T00:00:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ef171bf9ef4358707c53b75518d44af20f16755a384bc025069eaa3bcf9a6819","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-30T16:18:26Z","title_canon_sha256":"5745eed95e9ad65b8092b8767522b4f628f5dad55fd92df35f7ccf2a453b0fea"},"schema_version":"1.0","source":{"id":"1908.11820","kind":"arxiv","version":1}},"canonical_sha256":"355d4e258d23b5118eb925d9a42d33606185af5dd12841e5ad3591e6cae7839d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"355d4e258d23b5118eb925d9a42d33606185af5dd12841e5ad3591e6cae7839d","first_computed_at":"2026-07-05T00:00:46.561088Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:00:46.561088Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1yrYRe4XGKMGCj2dbOa8IK6kaOz/us3ld/ccrFkL3kbZYnEp9VlVvcmVWp2snDwX2sfFOf9dQf5QLmQ5J4SZAA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:00:46.561578Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.11820","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e9d6475f93cd77d3259589c31f1ed0540c7cea9d810f19cd522e94341cc6ec17","sha256:2c4857e992c5c76122cd0d7aa824f1fc0095526be7ea447a9b473a222ffb2579"],"state_sha256":"8e6cac3e003a1e82b4fbbd4de176120b5aee9ab4df8f05219a9f9f5df03b50a4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Kurj2gVgXJIhKrHxMPV+ui0PeOR2lwqEbGOjWxQAuwwuManHVlAfOgq8vBg+YiIOJmBIt3im6G0yVANuEEiOCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T02:13:17.175919Z","bundle_sha256":"b67d68ce6e43cf2db3e58d03250090df892f876403b1d36afc8a23afa2fb3024"}}